{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     1,
     29
    ]
   },
   "outputs": [],
   "source": [
    "class MyModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        # layer1\n",
    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=0)\n",
    "        self.batchnorm1 = nn.BatchNorm2d((32))\n",
    "        self.relu1 = nn.ReLU(inplace=True)\n",
    "        self.pooling1 = nn.MaxPool2d((2, 2))\n",
    "        # layer2\n",
    "        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=0)\n",
    "        self.batchnorm2 = nn.BatchNorm2d((64))\n",
    "        self.relu2 = nn.ReLU(inplace=True)\n",
    "        self.pooling2 = nn.MaxPool2d((2, 2))\n",
    "        # layer3\n",
    "        self.conv3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=0)\n",
    "        self.batchnorm3 = nn.BatchNorm2d((128))\n",
    "        self.relu3 = nn.ReLU(inplace=True)\n",
    "        self.pooling3 = nn.MaxPool2d((2, 2))\n",
    "        # layer4\n",
    "        self.conv4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=0)\n",
    "        self.batchnorm4 = nn.BatchNorm2d((256))\n",
    "        self.relu4 = nn.ReLU(inplace=True)\n",
    "        self.pooling4 = nn.MaxPool2d((2, 2))\n",
    "        # classifier\n",
    "        self.flatten = nn.Flatten()\n",
    "        # 六个类别\n",
    "        self.linear = nn.Linear(in_features=16384, out_features=6)\n",
    "        self.sigmoid = nn.Sigmoid()\n",
    "        \n",
    "    def forward(self, input):\n",
    "        # layer1\n",
    "        self.conv1_out = self.conv1(input)\n",
    "        self.batchnorm1_out = self.batchnorm1(self.conv1_out)\n",
    "        self.relu1_out = self.relu1(self.batchnorm1_out)\n",
    "        self.pooling1_out = self.pooling1(self.relu1_out)\n",
    "        # layer2\n",
    "        self.conv2_out = self.conv2(self.pooling1_out)\n",
    "        self.batchnorm2_out = self.batchnorm2(self.conv2_out)\n",
    "        self.relu2_out = self.relu2(self.batchnorm2_out)\n",
    "        self.pooling2_out = self.pooling2(self.relu2_out)\n",
    "        # layer3\n",
    "        self.conv3_out = self.conv3(self.pooling2_out)\n",
    "        self.batchnorm3_out = self.batchnorm3(self.conv3_out)\n",
    "        self.relu3_out = self.relu3(self.batchnorm3_out)\n",
    "        self.pooling3_out = self.pooling3(self.relu3_out)\n",
    "        # layer4\n",
    "        self.conv4_out = self.conv4(self.pooling3_out)\n",
    "        self.batchnorm4_out = self.batchnorm4(self.conv4_out)\n",
    "        self.relu4_out = self.relu4(self.batchnorm4_out)\n",
    "        # classifier\n",
    "        self.flatten_out = self.flatten(self.relu4_out)\n",
    "        self.linear_out = self.linear(self.flatten_out)\n",
    "        self.sigmoid_out = self.sigmoid(self.linear_out)\n",
    "        \n",
    "        output = self.sigmoid_out\n",
    "        \n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mymodel = MyModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1            [1, 32, 98, 98]             896\n",
      "       BatchNorm2d-2            [1, 32, 98, 98]              64\n",
      "              ReLU-3            [1, 32, 98, 98]               0\n",
      "         MaxPool2d-4            [1, 32, 49, 49]               0\n",
      "            Conv2d-5            [1, 64, 47, 47]          18,496\n",
      "       BatchNorm2d-6            [1, 64, 47, 47]             128\n",
      "              ReLU-7            [1, 64, 47, 47]               0\n",
      "         MaxPool2d-8            [1, 64, 23, 23]               0\n",
      "            Conv2d-9           [1, 128, 21, 21]          73,856\n",
      "      BatchNorm2d-10           [1, 128, 21, 21]             256\n",
      "             ReLU-11           [1, 128, 21, 21]               0\n",
      "        MaxPool2d-12           [1, 128, 10, 10]               0\n",
      "           Conv2d-13             [1, 256, 8, 8]         295,168\n",
      "      BatchNorm2d-14             [1, 256, 8, 8]             512\n",
      "             ReLU-15             [1, 256, 8, 8]               0\n",
      "          Flatten-16                 [1, 16384]               0\n",
      "           Linear-17                     [1, 6]          98,310\n",
      "          Sigmoid-18                     [1, 6]               0\n",
      "================================================================\n",
      "Total params: 487,686\n",
      "Trainable params: 487,686\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.11\n",
      "Forward/backward pass size (MB): 13.00\n",
      "Params size (MB): 1.86\n",
      "Estimated Total Size (MB): 14.98\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 模型可视化\n",
    "from torchsummary import summary\n",
    "summary(model=mymodel, input_size=[(3, 100, 100)], batch_size=1, device='cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集\n",
    "# 使用torchvisiond的datasets.ImageFolder类来导入本地的数据集\n",
    "# 使用transform来进行数据增强\n",
    "train_dataset = torchvision.datasets.ImageFolder(\n",
    "    root='F:/BaiduNetdiskDownload/数据集/新鲜水果/Datasets/train/',\n",
    "    transform=torchvision.transforms.Compose([\n",
    "        torchvision.transforms.Resize((100, 100)),\n",
    "        torchvision.transforms.RandomHorizontalFlip(),\n",
    "        torchvision.transforms.RandomRotation(20),\n",
    "        torchvision.transforms.ToTensor(),\n",
    "    ]))\n",
    "\n",
    "val_dataset = torchvision.datasets.ImageFolder(\n",
    "    root='F:/BaiduNetdiskDownload/数据集/新鲜水果/Datasets/test/',\n",
    "    transform=torchvision.transforms.Compose([\n",
    "        torchvision.transforms.Resize((100, 100)),\n",
    "        torchvision.transforms.ToTensor(),\n",
    "    ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据导入器\n",
    "train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=16, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "train_iter = iter(train_dataloader) # 将train_dataloader变成迭代器\n",
    "images, labels = train_iter.__next__()\n",
    "for i in range(32):\n",
    "    image = images[i]\n",
    "    label = labels[i]\n",
    "    image = np.transpose(image, (1, 2, 0))\n",
    "    plt.subplot(4, 8, i+1)\n",
    "    plt.imshow(image)\n",
    "    plt.axis('off')\n",
    "    plt.title(label.item())\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把数据导入器设置成无限的\n",
    "def get_infinite_dataloader(dataloader):\n",
    "    while True:\n",
    "        for item in dataloader:\n",
    "            yield item\n",
    "train_dataloader = get_infinite_dataloader(train_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MyModel(\n",
       "  (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (batchnorm1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu1): ReLU(inplace=True)\n",
       "  (pooling1): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (batchnorm2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu2): ReLU(inplace=True)\n",
       "  (pooling2): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (batchnorm3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu3): ReLU(inplace=True)\n",
       "  (pooling3): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "  (conv4): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))\n",
       "  (batchnorm4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (relu4): ReLU(inplace=True)\n",
       "  (pooling4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n",
       "  (flatten): Flatten()\n",
       "  (linear): Linear(in_features=16384, out_features=6, bias=True)\n",
       "  (sigmoid): Sigmoid()\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用GPU训练\n",
    "device = torch.device('cuda:0')\n",
    "mymodel.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:0, loss:1.8164331912994385\n",
      "epoch:0, total_val_acc:0.14936991035938263\n",
      "epoch:100, loss:1.2354270219802856\n",
      "epoch:200, loss:1.250704288482666\n",
      "epoch:300, loss:1.2941664457321167\n",
      "epoch:400, loss:1.1461578607559204\n",
      "epoch:400, total_val_acc:0.76352858543396\n",
      "epoch:500, loss:1.238399863243103\n",
      "epoch:600, loss:1.3331952095031738\n",
      "epoch:700, loss:1.180694580078125\n",
      "epoch:800, loss:1.3932068347930908\n",
      "epoch:800, total_val_acc:0.7527798414230347\n",
      "epoch:900, loss:1.275368332862854\n",
      "epoch:1000, loss:1.191064476966858\n",
      "epoch:1100, loss:1.2122267484664917\n",
      "epoch:1200, loss:1.2446492910385132\n",
      "epoch:1200, total_val_acc:0.8280208110809326\n",
      "epoch:1300, loss:1.2069618701934814\n",
      "epoch:1400, loss:1.2348933219909668\n",
      "epoch:1500, loss:1.3007185459136963\n",
      "epoch:1600, loss:1.2267975807189941\n",
      "epoch:1600, total_val_acc:0.8673091530799866\n"
     ]
    }
   ],
   "source": [
    "# 快乐训练咯\n",
    "# 先定义一下超参数\n",
    "total_epoch = 1600\n",
    "optimizer = torch.optim.Adam(mymodel.parameters(), lr=0.0005)\n",
    "\n",
    "def train():\n",
    "    mymodel.train()\n",
    "    # 取出数据\n",
    "    image, label = train_dataloader.__next__()\n",
    "    image = image.to(device)\n",
    "    label = label.to(device)\n",
    "    # 前向传播计算loss\n",
    "    output = mymodel(image)\n",
    "    loss = nn.CrossEntropyLoss()(output, label)\n",
    "    # 反向传播计算梯度\n",
    "    optimizer.zero_grad() # 梯度置零\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    # 打印loss\n",
    "    if i % 100 == 0:\n",
    "        print(f'epoch:{i}, loss:{loss.item()}')\n",
    "        \n",
    "def val():\n",
    "    mymodel.eval()\n",
    "    total_item = 0\n",
    "    correct_item = 0\n",
    "    for item in val_dataloader:\n",
    "        # 取出数据\n",
    "        image, label = item\n",
    "        image = image.to(device)\n",
    "        label = label.to(device)\n",
    "        # 计算模型输出\n",
    "        output = mymodel(image)\n",
    "        # 取出最大的输出作为结果\n",
    "        _, pred = torch.max(output, 1)\n",
    "        # 更新总预测数量\n",
    "        total_item += label.size(0)\n",
    "        # 如果相等则预测正确数量加1\n",
    "        correct_item += (pred==label).sum()   \n",
    "    total_val_acc = correct_item.true_divide(total_item)\n",
    "    print(f'epoch:{i}, total_val_acc:{total_val_acc.item()}')\n",
    "        \n",
    "for i in range(total_epoch+1):\n",
    "    train()\n",
    "    if i % 400 == 0:\n",
    "        val()\n",
    "\n",
    "# 保存模型\n",
    "torch.save(mymodel.state_dict(), 'fresh_fruits.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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